# Claudia Campos Week 8

## Electronic Lab Notebook

This Week's Analysis of Vibrio cholerae microarray data project:

Methods: The step by step instructions on the Bioinformatics Laboratory page wre followed.

• The raw data for the Vibrio cholerae was dowloaded.
• Scaling and centering began
• Insert a new Worksheet into your Excel file, and name it "scaled_centered".
• Go back to the "compiled_raw_data" worksheet, Select All and Copy. Go to your new "scaled_centered" worksheet, click on the upper, left-hand cell (cell A1) and Paste.
• Insert two rows in between the top row of headers and the first data row.
• In cell A2, type "Average" and in cell A3, type "StdDev".
• Compute the Average log ratio for each chip (each column of data). In cell B2, type the following equation:
```=AVERAGE(B4:B5224)
```

and press "Enter"

• Compute the Standard Deviation of the log ratios on each chip (each column of data). In cell B3, type the following equation:
```=STDEV(B4:B5224)
```

and press "Enter".

• Copy these two equations (cells B2 and B3) and paste them into the empty cells in the rest of the columns.
• Insert a new column to the right of each data column and label the top of the column as follows: A1_scaled_centered, A2_scaled_centered, etc.
• In cell C4, type the following equation:
```=(B4-\$B\$2)/\$B\$3
```
• Copy and paste this equation into the entire column.
• Repeat the scaling and centering equation for each of the columns of data. Be sure that your equation is correct for the column you are calculating.
• Statistical analysis began
• Insert a new worksheet and name it "statistics".
• Go back to the "scaling_centering" worksheet and copy the first column ("ID").
• Paste the data into the first column of your new "statistics" worksheet.
• Go back to the "scaling_centering" worksheet and copy Column C ("A1_scaled_centered).
• Go to your new worksheet and click on the B1 cell.
• Go to a new column on the right of your worksheet. Type the header "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C" into the top cell of the next three columns.
• Compute the average log fold change for the replicates for each patient by typing the equation:
```=AVERAGE(B2:E2)
```

into cell N2. Copy this equation and paste it into the rest of the column.

• Create the equation for patients B and C and paste it into their respective columns.
• Type the header "Avg_LogFC_all" into the first cell in the next empty column. Create the equation that will compute the average of the three previous averages you calculated and paste it into this entire column.
• Insert a new column next to the "Avg_LogFC_all" column that you computed in the previous step. Label the column "Tstat". Enter the equation:
```=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))
```
• Label the top cell in the next column "Pvalue". In the cell below the label, enter the equation:
```=TDIST(ABS(R2),2,2)
```
• Insert a new worksheet and name it "forGenMAPP".
• Go back to the "statistics" worksheet and Select All and Copy.
• Go to your new sheet and click on cell A1 and select Paste Special, click on the Values radio button, and click OK. We will now format this worksheet for import into GenMAPP.
• Select Columns B through Q (all the fold changes). Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.
• Select Columns R and S. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.
• Select Columns N through S and Cut. Select Column B by left-clicking on the "B" at the top of the column. Then right-click on the Column B header and select "Insert Cut Cells". This will insert the data without writing over your existing columns.
• Delete Rows 2 and 3 where it says "Average" and "StDev" so that your data rows with gene IDs are immediately below the header row 1.
• Insert a column to the right of the "ID" column. Type the header "SystemCode" into the top cell of this column. Fill the entire column (each cell) with the letter "N".
• Select the menu item File > Save As, and choose "Text (Tab-delimited) (*.txt)" from the file type drop-down menu.

Results:Ready for GenMAPP. All the data is overwhelming, which is where GenMAPP will come in to simplify it all and it will make sense.

Interpretations: Most of the genes had a p value less than 0.05 indicating significance. All of the genes had some amount of significant change from zero. It will be interesting to see the results GenMAPP produces.

Sanity Check

• How many genes have p value < 0.05? 5135
• What about p < 0.01? 4892
• What about p < 0.001? 1765
• What about p < 0.0001? 2
• Pvalue filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there? 5003
• Pvalue filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there? 5063
• What about an average log fold change of > 0.25 or < -0.25? 5134
• What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method? They used the method of
• Merrell et al. (2002) report that genes with IDs: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583 were all significantly changed in their data. Look these genes up in your spreadsheet? What are their fold changes and p values? Are they significantly changed in our analysis? Fold changes (respectively): 1.65, -0.28, 1.59, 1.92, -1.11, -0.17, -2.40, 1.06 P values (respectively): 0.0474, 0.1636, 0.0463, 0.0139, 0.0003, 0.3350, 0.0130, 0.1011 All except VC0941, VC0468, and VCA0583 were significantly changed since their p values were <0.05.